Journal
MOLECULAR & CELLULAR PROTEOMICS
Volume 18, Issue 12, Pages 2506-2515Publisher
ELSEVIER
DOI: 10.1074/mcp.TIR119.001481
Keywords
Drug targets; algorithms; biostatistics; tandem mass spectrometry; mathematical modeling; functional data analysis; thermal proteome profiling
Categories
Funding
- Cambridge Cancer Centre studentship
- Deutsche Forschungsgemeinschaft [SFB 1036]
- European Commission's H2020 Programme, Collaborative research project SOUND [633974]
- European Molecular Biology Laboratory International PhD Program
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Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present nonparametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP data sets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. An open source software implementation of NPARC is provided. A method for nonparametric analysis of response curves from thermal proteome profiling (TPP) experiments is proposed. The approach achieves high proteome coverage and sensitivity while controlling the false discovery-rate. Free open source software for NPARC is provided.
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